Classification of Eimeria species from digital micrographies using CNNs
Classification of Eimeria species from digital micrographies using CNNs
- Author(s): D.F. Monge and C.A. Beltran
- DOI: 10.1049/cp.2019.0254
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- Author(s): D.F. Monge and C.A. Beltran Source: 10th International Conference on Pattern Recognition Systems, 2019 p. 16 (88 – 91)
- Conference: 10th International Conference on Pattern Recognition Systems
- DOI: 10.1049/cp.2019.0254
- ISBN: 978-1-83953-108-8
- Location: Tours, France
- Conference date: 8-10 July 2019
- Format: PDF
This paper presents a model for the classification of the seven species of avian Eimeria, the protozoan parasite that causes avian coccidiosis. Digital micrographs dataset consists of 4485 isolated samples of the various species of oocytes (status of the Eimeria protozoon in which the internal structure is visually different in each species). The proposed solution applied a convolutional neural network architecture for the classification of the oocytes. Different experiments were developed to enhance the previous results of the literature, and with our proposal, we obtained a better average of correct classification for the seven species, reaching 90.42% of precision. Finally, with our strategy we used for the first time a CNN model over the Eimeria dataset, demonstrating that CNN is a robust technique for artificial vision problems.
Inspec keywords: computer vision; convolutional neural nets; biology computing; image classification
Subjects: Computer vision and image processing techniques; Biology and medical computing; Neural computing techniques; Image recognition
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